Non-Invasive Neonatal Jaundice Detection Using Image Processing and Machine Learning

Authors

  • Lakshmi A Menon Department of Electronics and Biomedical engineering, Adi Shankara Institute of Engineering and technology, APJ Abdul Kalam Technological University, India Author
  • Mary Ansteena Joseph Department of Electronics and Biomedical engineering, Adi Shankara Institute of Engineering and technology, APJ Abdul Kalam Technological University Author
  • Navya N A Department of Electronics and Biomedical engineering, Adi Shankara Institute of Engineering and technology, APJ Abdul Kalam Technological University Author
  • Afsalu Rahman S Department of Electronics and Biomedical engineering, Adi Shankara Institute of Engineering and technology, APJ Abdul Kalam Technological University Author
  • Surya D Department of Electronics and Biomedical engineering, Adi Shankara Institute of Engineering and technology, APJ Abdul Kalam Technological University Author

DOI:

https://doi.org/10.21467/proceedings.7.5.8

Keywords:

Neonatal hyperbilirubinemia, Image Processing, XGBoost

Abstract

Neonatal hyperbilirubinemia, marked by elevated bilirubin levels, can lead to jaundice and severe complications if untreated. Conventional diagnosis involves invasive blood sampling, which is time-intensive and stressful for newborns. This study presents a non-invasive bilirubin estimation method using image processing, offering a faster, more accessible alternative. Key steps include skin detection, region of interest (ROI) extraction, and conversion to the YCbCr color space to enhance sensitivity to bilirubin-induced color shifts, especially in the Cb channel. Machine learning algorithms: K-Nearest Neighbors (k-NN), Random Forest (RF), and XGBoost, were evaluated, with XGBoost achieving 98% accuracy and the lowest mean squared error (MSE). This approach enables rapid jaundice detection, reducing reliance on repeated blood tests. Its integration into clinical practices and home-based monitoring systems offers the potential for early diagnosis and timely intervention, significantly improving neonatal healthcare outcomes.

References

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Published

2025-09-23

Issue

Vol. 7 No. 5 (2025): Proceedings of the International Conference on Innovations in Mechanical Robotics Computing and Biomedical Engineering: ICIMRBE 2025

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
L. A. Menon, M. A. Joseph, N. N A, A. Rahman S, and D. Surya, “Non-Invasive Neonatal Jaundice Detection Using Image Processing and Machine Learning”, AIJR Proc., vol. 7, no. 5, pp. 53–61, Sep. 2025, doi: 10.21467/proceedings.7.5.8.